CS 661: System Simulation
Spring Semester at NJIT
Tuesday Evenings, 6:00 - 9:05pm
Instructor
Prof. Jim Calvin
Office: 4409 GITC
Phone: (973) 596-3378
e-mail: calvin@njit.edu
URL: web.njit.edu/∼calvin
Description
Simulation is one of the most widely used tools for analyzing complex processes and systems. Its
use in engineering and business has increased dramatically in recent years, in part because of the
increased power of personal computers and workstations, on which the vast majority of simulations
are carried out. Examples of the use of simulation in engineering include the design and analysis
of computer systems, networking, web-based systems, and fault-tolerant computing. The uses of
simulation in a business environment include evaluating alternative operational policies, viewing
the impact of changes in personnel or equipment on business performance, assessing the capabilities
of a proposed factory, performing risk analysis of a proposed business plan, and pricing financial
instruments.
This course covers the use of simulation as a tool for analyzing engineering and business problems.
The two primary goals of the course are to learn how to plan, build and use simulation models and
to develop an understanding of when simulation is an appropriate tool for analysis. Much of the
work in the course will involve learning the mathematical and software tools for building simulation
models, performing experiments with them, and interpreting the results.
Although the focus of the course is on general simulation concepts and techniques, independent
of a simulation package, we will also illustrate some of the material using the software package
ARENA, a popular simulation package. ARENA is well suited for creating large and detailed
discrete-event simulation models. We will also use Excel to carry out some Monte Carlo simulations.
The course will cover many applications of simulation, including computer-performance modeling,
manufacturing, and finance.
Prerequisites
An undergraduate or graduate calculus-based course in probability and statistics at the level of
Math 244 or Math 333, and working knowledge of at least one higher-level programming language
(e.g., C or Java).
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Tentative Course Outline
1. Introduction to simulation modeling, and review of basic probability and statistics (2 weeks)
2. Fundamental simulation concepts (1 week)
3. Probability distributions and input modeling (2 weeks)
4. Generating uniform and non-uniform random variates (2 weeks)
5. Building simulation models using Arena (2 weeks)
6. Output data analysis for simulations (1 week)
7. Monte Carlo simulation, including project management and finance (2 weeks)
8. Variance-reduction techniques (2 weeks)
Throughout the course, we will cover many topics in finance, including stochastic models for risky
asset prices (geometric Brownian motion, jump-diffusion models, Markov regime-switching models),
interest-rate models, portfolio valuation, pricing options (including exotic path-dependent options),
estimating value-at-risk, and variance-reduction techniques for pricing path-dependent options.
Textbook
There are two alternate texts for the course:
• A. M. Law, Simulation Modeling and Analysis, 4th Edition, McGraw-Hill, 2007, ISBN:
0073294411.
• P. Glasserman, Monte Carlo Methods in Financial Engineering, Springer, 2003, ISBN: 0387004513.
Students interested in finance should buy the Glasserman book. All other students should buy the
Law book. Students only need to buy one of the books.
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CS704: Scheduling
Spring Semester at NJIT
Instructor
Prof. Joseph Leung
Office: 4202 GITC
Phone: (973) 596-3387
e-mail: leung@oak.njit.edu
URL: web.njit.edu/∼leung
WeeklyListing of Topics
1. Overview of Scheduling. The 3-field Notation. Tutorial on Complexity Theory.
2. Tutorial on Complexity Theory.
3. The Makespan Criteria. UET Task Systems. The Coffman-Graham Algorithm.
4. Hu’s Algorithm. The LPT Rule. Preemptive Scheduling. McNaughton’s Rule. MuntzCoffman Algorithm.
5. List Scheduling. Anomaly of List Scheduling. Comparison of List, Nonpreemptive and Preemptive Scheduling.
6. Optimal Online Scheduling. Heuristics. Competitive Ratios.
7. Midterm Exam.
8. Mean Weighted Flow Time. SPT Rule. Bicriteria Scheduling: (1) Minimzing Makespan
Subject to Minimum Mean Flow Time.
9. Bicriteria Scheduling (2) Minimizing Mean Flow Time Subject to Minimum Makespan. Minimizing Mean Flow Time Subject to Release Time and Deadline Constraint.
10. Maximum Lateness Objective. Lawler’s Algorithm. Sahni’s Algorithm. Horn’s Network Flow
Algorithm.
11. Hodgson-Moore Algorithm. Tardiness Objective. Lawler’s Pseudo-Polynomial Time Algorithm.
12. Midterm Exam.
13. Open Shop, Flow Shop and Job Shop
14. Review
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CS: Optimization
Fall 2011 at NJIT
Instructor
Prof. Jim Calvin
Office: 4409 GITC
Phone: (973) 596-3378
e-mail: calvin@njit.edu
URL: web.njit.edu/∼calvin
Description
A survey of methods and applications of global optimization.
Prerequisites
An undergraduate or graduate calculus-based course in probability and statistics at the level of
Math 244 or Math 333, and working knowledge of at least one higher-level programming language
(e.g., C or Java).
Main Topics
1. Global optimization, randomized algorithms; simulated annealing; evolutionary algorithms.
2. Methods based on stochastic models; average-case analysis.
3. Complexity of global optimization.
4. Optimization with noise-corrupted function evaluations.
5. Simulation-based optimization.
Textbook
A. Zhigljavsky and A. Z̆ilinskas, Stochastic Global Optimization, Springer, 2008.
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